# pip install --upgrade pycaret scipy
# Importación de librerías esenciales para manipulación y análisis de datos
import pandas as pd
import numpy as np
# Importación de librerías para visualización de datos
import matplotlib.pyplot as plt
import seaborn as sns
# Herramientas de preprocesamiento de sklearn
from sklearn.preprocessing import MinMaxScaler, StandardScaler, LabelEncoder
# Modelos y herramientas de selección y evaluación de sklearn
from sklearn.feature_selection import f_classif, SelectKBest
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import LassoCV
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier, BaggingClassifier
from sklearn.impute import KNNImputer
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error, median_absolute_error, explained_variance_score
from sklearn.pipeline import Pipeline
# Configuraciones específicas para mejorar la visualización en cuadernos Jupyter
pd.set_option('display.max_rows', 50) # Limitar el máximo de filas visibles para mejorar la performance
pd.set_option('display.max_columns', None) # Mostrar todas las columnas disponibles
# Ajustes del sistema y manejo de módulos personalizados
import sys
sys.path.append('../src/utils')
# Importación de módulos personalizados para funcionalidades específicas
from utils import (
load_data,
plot_boxplot,
plot_category_counts,
plot_heatmap
)
from preprocess import *
from modeling_dev import *
# Importaciones específicas de PyCaret para facilitar el modelado y la clasificación
from pycaret.classification import *
from pycaret.utils import version
version() # Muestra la versión de PyCaret para asegurar compatibilidad
'3.3.2'
df = load_data('../data/processed/prediction_dataset_complete.csv')
print("Dimensiones del DataFrame cargado:", df.shape)
Datos cargados con dimensiones iniciales: (717, 66) Dimensiones del DataFrame cargado: (717, 66)
df.head()
| edad | sexo | altura | peso | num calzado | articulacion | localizacion | lado | pace_walk | velocidad_walk | step rate_walk | stride length_walk | shock_walk | impact gs_walk | braking gs_walk | footstrike type_walk | pronation excursion_walk | contact ratio_walk | total force rate_walk | step length_walk | pronation excursion (mp->to)_walk | stance excursion (fs->mp)_walk | stance excursion (mp->to)_walk | m1 hipermovil | thomas psoas | thomas rf | thomas tfl | ober | arco aplanado | arco elevado | m1 dfx | m5 hipermovil | arco transverso disminuido | m1 pfx | arco transverso aumentado | hlf | hl | hr | hav | index minus | tfi | tfe | tti | tte | ober friccion | popliteo | t_hintermann | jack normal | jack no reconstruye | pronacion no disponible | 2heel raise | heel raise | fpi_total_i | fpi_total_d | tibia vara proximal | tibia vara distal | rotula divergente | rotula convergente | rotula ascendida | genu valgo | genu varo | genu recurvatum | genu flexum | lunge | imc | zona afectada | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 36 | 0 | 173 | 65.0 | 40.0 | rodilla | medial | b | 1.291600 | 5.0 | 108.456898 | 1.4267 | 3.566339 | 2.310324 | 2.610172 | 6.314815 | -11.106498 | 63.724990 | 24.952120 | 0.7133 | -14.725130 | 19.445907 | 62.315404 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 3 | 0 | 3 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | -1.0 | -1.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 1 | 21.72 | rodilla_medial_b |
| 1 | 35 | 1 | 181 | 78.0 | 44.0 | sin afectacion | no especificado | no especificado | 1.370079 | 5.0 | 114.767847 | 1.4312 | 3.119538 | 1.900867 | 2.387259 | 6.948276 | -8.176466 | 62.112399 | 30.589598 | 0.7156 | 0.962024 | 17.340533 | 55.030184 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7.0 | 7.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 23.81 | sin patologia |
| 2 | 45 | 1 | 189 | 88.0 | 46.0 | sin afectacion | no especificado | no especificado | 1.371909 | 5.0 | 100.149777 | 1.6437 | 2.368494 | 1.597828 | 1.558757 | 10.680851 | -4.411249 | 64.721319 | 32.726926 | 0.8218 | 0.192496 | 8.006323 | 77.799223 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6.0 | 5.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 24.64 | sin patologia |
| 3 | 43 | 1 | 182 | 70.0 | 44.0 | pie | medial | d | 1.318286 | 5.0 | 103.976334 | 1.5381 | 2.667928 | 1.708754 | 1.985392 | 11.180000 | -4.351264 | 63.545858 | 28.220870 | 0.7690 | 6.506884 | 7.471882 | 81.783758 | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 7.0 | 7.0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 21.13 | pie-tobillo_medial_d |
| 4 | 41 | 1 | 184 | 90.0 | 43.0 | pierna | posterior | b | 1.362836 | 5.0 | 99.062660 | 1.6541 | 3.610964 | 2.665554 | 2.369300 | 7.900000 | -10.180634 | 67.074954 | 33.841236 | 0.8270 | -6.480006 | 14.944068 | 89.849386 | 0 | 0 | 3 | 1 | 0 | 0 | 3 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 3.0 | 0.0 | 1 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 3 | 26.58 | pierna_posterior_b |
print(f"Nº de categorías únicas en 'articulacion' antes de la transformación: {df['articulacion'].nunique()}")
Nº de categorías únicas en 'articulacion' antes de la transformación: 8
# Agrupación de categorías menos frecuentes en una nueva categoría llamada 'otro'
df['articulacion'] = df['articulacion'].replace(['pierna', 'cadera', 'espalda', 'sin afectacion', 'muslo', 'complejo'], 'otro')
print(f"Número de categorías únicas en 'articulacion' después de la transformación: {df['articulacion'].nunique()}")
Número de categorías únicas en 'articulacion' después de la transformación: 4
# Eliminación de columnas que no se utilizarán
df = df.drop(columns=['localizacion', 'lado', 'zona afectada'])
plot_boxplot(df) # Visualización
# Preparación de los datos
X = df.drop('articulacion', axis=1) # Eliminar la columna objetivo del conjunto de características
y = df['articulacion'] # Definir el conjunto objetivo
# División del conjunto de datos en entrenamiento y test
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y
)
# Visualización de las dimensiones de los conjuntos de datos
print(f'Dimensiones del dataset de train: {X_train.shape} | {y_train.shape}')
print(f'Dimensiones del dataset de validación: {X_test.shape} | {y_test.shape}')
X_train.head()
Dimensiones del dataset de train: (573, 62) | (573,) Dimensiones del dataset de validación: (144, 62) | (144,)
| edad | sexo | altura | peso | num calzado | pace_walk | velocidad_walk | step rate_walk | stride length_walk | shock_walk | impact gs_walk | braking gs_walk | footstrike type_walk | pronation excursion_walk | contact ratio_walk | total force rate_walk | step length_walk | pronation excursion (mp->to)_walk | stance excursion (fs->mp)_walk | stance excursion (mp->to)_walk | m1 hipermovil | thomas psoas | thomas rf | thomas tfl | ober | arco aplanado | arco elevado | m1 dfx | m5 hipermovil | arco transverso disminuido | m1 pfx | arco transverso aumentado | hlf | hl | hr | hav | index minus | tfi | tfe | tti | tte | ober friccion | popliteo | t_hintermann | jack normal | jack no reconstruye | pronacion no disponible | 2heel raise | heel raise | fpi_total_i | fpi_total_d | tibia vara proximal | tibia vara distal | rotula divergente | rotula convergente | rotula ascendida | genu valgo | genu varo | genu recurvatum | genu flexum | lunge | imc | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 75 | 45 | 1 | 178 | 80.0 | 44.1 | 1.431927 | 5.0 | 112.891509 | 1.5229 | 2.326613 | 2.014790 | 1.131385 | 12.089286 | -1.934450 | 63.539849 | 29.526575 | 0.7614 | -12.226850 | 5.191136 | 80.358554 | 0 | 0 | 3 | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 0 | 0 | 0 | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 4.0 | 5.0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 25.25 |
| 256 | 43 | 1 | 170 | 71.0 | 42.0 | 1.316527 | 5.0 | 113.289981 | 1.4059 | 1.859483 | 0.987038 | 1.464532 | 11.022472 | -3.463675 | 64.150504 | 28.362304 | 0.7030 | -8.461037 | 7.424516 | 74.429788 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 0 | 0 | 1 | 3 | 0 | 3 | 0 | 0 | 0 | 0 | -1.0 | -1.0 | 3 | 3 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 24.57 |
| 626 | 30 | 1 | 178 | 74.0 | 41.0 | 1.383853 | 5.0 | 108.546666 | 1.5321 | 1.973759 | 1.317979 | 1.445499 | 9.339623 | -9.473472 | 64.472397 | 16.185594 | 0.7661 | -2.814574 | 11.068998 | 79.124419 | 0 | 0 | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 0 | 3 | 0 | 3 | 0 | 3 | 0 | 0 | 0 | 0 | -4.0 | -6.0 | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 23.36 |
| 8 | 50 | 0 | 165 | 67.0 | 37.0 | 0.083356 | 5.0 | 124.494250 | 0.0803 | 2.660461 | 1.821702 | 1.860127 | 11.419355 | -6.135594 | 66.482163 | 16.611606 | 0.0401 | -1.784790 | 6.559235 | 61.928437 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 9.0 | 9.0 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 24.61 |
| 149 | 47 | 0 | 170 | 60.0 | 40.0 | 1.381579 | 5.0 | 112.642498 | 1.4708 | 2.907667 | 2.121389 | 1.946341 | 12.375000 | -4.351580 | 62.359526 | 28.840264 | 0.7354 | -7.837623 | 4.836789 | 79.862173 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 5.0 | 1.0 | 0 | 2 | 0 | 0 | 0 | 3 | 0 | 3 | 0 | 0 | 20.76 |
# Guardamos
# X_train.to_csv('../data/processed/sin_escalar/X_train.csv', sep=';', decimal='.', index=False)
# X_test.to_csv('../data/processed/sin_escalar/X_test.csv', sep=';', decimal='.', index=False)
# y_train.to_csv('../data/processed/sin_escalar/y_train.csv', sep=';', decimal='.', index=False)
# y_test.to_csv('../data/processed/sin_escalar/y_test.csv', sep=';', decimal='.', index=False)
# Cargar los dataset
# X_train = pd.read_csv('../data/processed/sin_escalar/X_train.csv', sep=';', decimal='.')
# X_test = pd.read_csv('../data/processed/sin_escalar/X_test.csv', sep=';', decimal='.')
# y_train = pd.read_csv('../data/processed/sin_escalar/y_train.csv', sep=';', decimal='.')
# y_test = pd.read_csv('../data/processed/sin_escalar/y_test.csv', sep=';', decimal='.')
X_train.head()
| edad | sexo | altura | peso | num calzado | pace_walk | velocidad_walk | step rate_walk | stride length_walk | shock_walk | impact gs_walk | braking gs_walk | footstrike type_walk | pronation excursion_walk | contact ratio_walk | total force rate_walk | step length_walk | pronation excursion (mp->to)_walk | stance excursion (fs->mp)_walk | stance excursion (mp->to)_walk | m1 hipermovil | thomas psoas | thomas rf | thomas tfl | ober | arco aplanado | arco elevado | m1 dfx | m5 hipermovil | arco transverso disminuido | m1 pfx | arco transverso aumentado | hlf | hl | hr | hav | index minus | tfi | tfe | tti | tte | ober friccion | popliteo | t_hintermann | jack normal | jack no reconstruye | pronacion no disponible | 2heel raise | heel raise | fpi_total_i | fpi_total_d | tibia vara proximal | tibia vara distal | rotula divergente | rotula convergente | rotula ascendida | genu valgo | genu varo | genu recurvatum | genu flexum | lunge | imc | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 75 | 45 | 1 | 178 | 80.0 | 44.1 | 1.431927 | 5.0 | 112.891509 | 1.5229 | 2.326613 | 2.014790 | 1.131385 | 12.089286 | -1.934450 | 63.539849 | 29.526575 | 0.7614 | -12.226850 | 5.191136 | 80.358554 | 0 | 0 | 3 | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 0 | 0 | 0 | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 4.0 | 5.0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 25.25 |
| 256 | 43 | 1 | 170 | 71.0 | 42.0 | 1.316527 | 5.0 | 113.289981 | 1.4059 | 1.859483 | 0.987038 | 1.464532 | 11.022472 | -3.463675 | 64.150504 | 28.362304 | 0.7030 | -8.461037 | 7.424516 | 74.429788 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 0 | 0 | 1 | 3 | 0 | 3 | 0 | 0 | 0 | 0 | -1.0 | -1.0 | 3 | 3 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 24.57 |
| 626 | 30 | 1 | 178 | 74.0 | 41.0 | 1.383853 | 5.0 | 108.546666 | 1.5321 | 1.973759 | 1.317979 | 1.445499 | 9.339623 | -9.473472 | 64.472397 | 16.185594 | 0.7661 | -2.814574 | 11.068998 | 79.124419 | 0 | 0 | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 3 | 0 | 3 | 0 | 3 | 0 | 3 | 0 | 0 | 0 | 0 | -4.0 | -6.0 | 3 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 23.36 |
| 8 | 50 | 0 | 165 | 67.0 | 37.0 | 0.083356 | 5.0 | 124.494250 | 0.0803 | 2.660461 | 1.821702 | 1.860127 | 11.419355 | -6.135594 | 66.482163 | 16.611606 | 0.0401 | -1.784790 | 6.559235 | 61.928437 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 9.0 | 9.0 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 24.61 |
| 149 | 47 | 0 | 170 | 60.0 | 40.0 | 1.381579 | 5.0 | 112.642498 | 1.4708 | 2.907667 | 2.121389 | 1.946341 | 12.375000 | -4.351580 | 62.359526 | 28.840264 | 0.7354 | -7.837623 | 4.836789 | 79.862173 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 5.0 | 1.0 | 0 | 2 | 0 | 0 | 0 | 3 | 0 | 3 | 0 | 0 | 20.76 |
# Histograma del conjunto de entrenamiento
X_train.hist(bins=20, figsize=(26, 24), color='royalblue')
plt.show()
# Estadísticas descriptivas del conjunto de entrenamiento
display(X_train.describe().round(3))
| edad | sexo | altura | peso | num calzado | pace_walk | velocidad_walk | step rate_walk | stride length_walk | shock_walk | impact gs_walk | braking gs_walk | footstrike type_walk | pronation excursion_walk | contact ratio_walk | total force rate_walk | step length_walk | pronation excursion (mp->to)_walk | stance excursion (fs->mp)_walk | stance excursion (mp->to)_walk | m1 hipermovil | thomas psoas | thomas rf | thomas tfl | ober | arco aplanado | arco elevado | m1 dfx | m5 hipermovil | arco transverso disminuido | m1 pfx | arco transverso aumentado | hlf | hl | hr | hav | index minus | tfi | tfe | tti | tte | ober friccion | popliteo | t_hintermann | jack normal | jack no reconstruye | pronacion no disponible | 2heel raise | heel raise | fpi_total_i | fpi_total_d | tibia vara proximal | tibia vara distal | rotula divergente | rotula convergente | rotula ascendida | genu valgo | genu varo | genu recurvatum | genu flexum | lunge | imc | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.000 | 573.00 | 573.000 | 573.000 | 573.000 |
| mean | 39.215 | 0.667 | 173.328 | 71.631 | 41.719 | 1.347 | 4.975 | 111.153 | 1.456 | 2.315 | 1.546 | 1.631 | 8.301 | -7.295 | 63.490 | 21.937 | 0.773 | -3.521 | 14.615 | 69.521 | 0.635 | 0.442 | 0.916 | 0.696 | 0.070 | 0.658 | 0.707 | 0.527 | 0.079 | 0.037 | 0.949 | 0.272 | 0.127 | 0.653 | 0.073 | 0.635 | 2.825 | 0.445 | 0.911 | 0.052 | 0.712 | 0.026 | 0.805 | 0.133 | 1.965 | 0.291 | 0.091 | 0.031 | 0.012 | 3.330 | 3.176 | 0.749 | 0.576 | 0.131 | 0.253 | 0.021 | 0.773 | 0.649 | 0.55 | 0.304 | 0.935 | 23.736 |
| std | 13.544 | 0.472 | 8.911 | 13.583 | 2.625 | 0.154 | 0.155 | 7.158 | 0.160 | 0.875 | 0.671 | 0.638 | 2.613 | 5.026 | 1.925 | 7.771 | 0.542 | 7.341 | 6.712 | 9.805 | 1.207 | 1.022 | 1.347 | 1.155 | 0.449 | 1.223 | 1.267 | 1.108 | 0.472 | 0.296 | 1.380 | 0.846 | 0.557 | 1.154 | 0.440 | 1.202 | 0.698 | 0.991 | 1.299 | 0.380 | 1.244 | 0.260 | 1.302 | 0.585 | 1.348 | 0.805 | 0.500 | 0.263 | 0.150 | 4.255 | 4.332 | 1.264 | 1.140 | 0.587 | 0.811 | 0.250 | 1.307 | 1.225 | 1.12 | 0.676 | 1.301 | 3.502 |
| min | 15.000 | 0.000 | 148.000 | 39.000 | 36.000 | 0.083 | 4.000 | 85.079 | 0.080 | 0.750 | 0.445 | 0.587 | 1.250 | -24.901 | 41.133 | 5.952 | 0.040 | -22.855 | -0.159 | 32.312 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | -11.000 | -8.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.00 | 0.000 | 0.000 | 15.040 |
| 25% | 29.000 | 0.000 | 168.000 | 62.000 | 40.000 | 1.361 | 5.000 | 106.987 | 1.412 | 1.746 | 1.132 | 1.209 | 6.173 | -9.849 | 62.619 | 14.845 | 0.706 | -8.441 | 9.161 | 62.430 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.00 | 0.000 | 0.000 | 21.260 |
| 50% | 39.000 | 1.000 | 173.000 | 71.000 | 42.000 | 1.371 | 5.000 | 110.876 | 1.477 | 2.083 | 1.364 | 1.496 | 8.640 | -6.237 | 63.627 | 19.978 | 0.739 | -3.532 | 13.729 | 70.310 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 3.000 | 3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.00 | 0.000 | 0.000 | 23.410 |
| 75% | 48.000 | 1.000 | 180.000 | 80.000 | 44.000 | 1.379 | 5.000 | 114.822 | 1.531 | 2.655 | 1.786 | 1.866 | 10.358 | -3.663 | 64.425 | 27.978 | 0.767 | 1.047 | 19.983 | 76.695 | 0.000 | 0.000 | 3.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 3.000 | 0.000 | 0.000 | 1.000 | 0.000 | 0.000 | 3.000 | 0.000 | 3.000 | 0.000 | 1.000 | 0.000 | 3.000 | 0.000 | 3.000 | 0.000 | 0.000 | 0.000 | 0.000 | 6.000 | 6.000 | 1.000 | 0.000 | 0.000 | 0.000 | 0.000 | 3.000 | 0.000 | 0.00 | 0.000 | 3.000 | 25.610 |
| max | 80.000 | 1.000 | 197.000 | 135.000 | 49.000 | 2.794 | 5.000 | 165.523 | 2.025 | 8.329 | 6.403 | 5.236 | 15.404 | 22.671 | 71.494 | 52.165 | 7.664 | 19.832 | 37.078 | 92.868 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 12.000 | 12.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.000 | 3.00 | 3.000 | 3.000 | 41.670 |
# Cálculo de percentiles críticos para características específicas
percentil_98_pace = df['pace_walk'].quantile(0.98)
percentil_98_stride_length = df['stride length_walk'].quantile(0.98)
# Filtrado de registros que superen el percentil 98 en características específicas
high_pace_walk = df[df['pace_walk'] > percentil_98_pace]
high_stride_length_walk = df[df['stride length_walk'] > percentil_98_stride_length]
# Visualización de los resultados filtrados
print("Registros con pace_walk alto:")
print(high_pace_walk[['pace_walk', 'stride length_walk']])
print("\nRegistros con stride length_walk alto:")
print(high_stride_length_walk[['pace_walk', 'stride length_walk']])
Registros con pace_walk alto:
pace_walk stride length_walk
24 1.466770 1.697700
108 1.460815 1.694800
127 1.495747 1.471800
128 1.495747 1.471800
135 1.492680 1.574900
248 1.482486 1.357700
281 1.467390 1.629000
394 1.477679 1.566000
403 1.455382 1.608100
424 1.486504 1.610200
554 2.325577 1.941543
571 1.907261 1.727432
572 1.907261 1.727432
574 2.793561 2.024550
676 1.454388 1.672300
Registros con stride length_walk alto:
pace_walk stride length_walk
24 1.466770 1.697700
108 1.460815 1.694800
220 1.368494 1.717100
239 1.404306 1.688000
240 1.404306 1.688000
241 1.404306 1.688000
343 1.373040 1.811300
344 1.373040 1.811300
552 1.349389 1.764691
554 2.325577 1.941543
571 1.907261 1.727432
572 1.907261 1.727432
574 2.793561 2.024550
628 1.373276 1.673800
674 1.392411 1.748000
Optamos por conservar los registros debido a que parecen legítimos y representativos de un subgrupo específico.
encoder = LabelEncoder()
y_train_encoded = encoder.fit_transform(y_train)
y_test_encoded = encoder.transform(y_test)
Para los modelos Random Forest y Decision Tree, que no son sensibles al escalado de las características, ya que realizan divisiones basadas en el orden y no en la magnitud, no escalaremos los datos.
plot_category_counts(y_train, 'Distribución de Categorías en el Dataset')
# Eliminar características constantes
X_train_var = X_train.loc[:, X_train.var() != 0] # En X_train es la columna 'pnca ap valgo'
# Suponiendo que X_train es tu conjunto de entrenamiento y y_train son las etiquetas
selector = SelectKBest(score_func=f_classif, k='all') # 'all' para seleccionar todas las características y ver sus puntuaciones
selector.fit(X_train_var, y_train)
# Muestra las puntuaciones de ANOVA para cada característica
print("Puntuaciones F de ANOVA:", selector.scores_)
Puntuaciones F de ANOVA: [1.06646085 4.81089112 2.94041921 1.82980159 2.82588555 0.86354468 0.04679907 1.44185914 1.717855 3.10212622 3.28431398 2.21912517 0.21422796 1.65781819 1.59975547 8.1668386 0.47655017 0.28126439 0.09928221 1.70063061 0.26507218 4.39322399 1.94683702 4.76807426 1.57051599 0.72608804 0.7131213 2.30370136 0.46115443 2.55732254 0.54754538 0.12844644 1.74342114 1.18480545 1.35850443 2.46706741 5.8887358 1.28743635 1.00568414 1.38860961 0.51157749 1.56433242 0.41815726 0.77808872 2.14462007 2.96618735 2.34866709 0.51719317 0.54210066 0.86955965 0.45150191 2.27720956 0.7994806 1.46988581 0.71711985 0.04800951 0.87754986 0.11467424 1.58977147 0.17002249 0.1300058 1.3980537 ]
Las características con las puntuaciones F más altas son más relevantes para predecir la variable objetivo. Por ejemplo, los valores mayores (como 8.20979186 para total force rate_walk) indican una mayor relevancia.
X_train_with_target = X_train.copy()
X_train_with_target['Target'] = y_train_encoded
corr_matrix = X_train_with_target.corr()
plot_heatmap(corr_matrix, figsize=(18, 14), cmap="GnBu", title='', annot=False)
threshold = 0.5 # Umbral de correlación
# Encuentra pares con una correlación superior al umbral
strong_pairs = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))
strong_pairs = strong_pairs.stack().reset_index()
strong_pairs.columns = ['variable1', 'variable2', 'correlation']
strong_pairs = strong_pairs[strong_pairs['correlation'] > threshold] # Filtrar pares con correlación fuerte
strong_pairs = strong_pairs.sort_values('correlation', ascending=False) # Ordenar por correlación
strong_pairs
| variable1 | variable2 | correlation | |
|---|---|---|---|
| 522 | shock_walk | impact gs_walk | 0.927667 |
| 523 | shock_walk | braking gs_walk | 0.918198 |
| 1862 | fpi_total_i | fpi_total_d | 0.902056 |
| 124 | altura | num calzado | 0.843144 |
| 240 | peso | imc | 0.839313 |
| 302 | pace_walk | stride length_walk | 0.819646 |
| 64 | sexo | num calzado | 0.771023 |
| 575 | impact gs_walk | braking gs_walk | 0.708804 |
| 183 | peso | num calzado | 0.683861 |
| 62 | sexo | altura | 0.682605 |
| 123 | altura | peso | 0.636667 |
| 63 | sexo | peso | 0.558533 |
| 684 | footstrike type_walk | stance excursion (mp->to)_walk | 0.555707 |
| 678 | footstrike type_walk | pronation excursion_walk | 0.555232 |
Las altas correlaciones entre características (como shock_walk y impact gs_walk) sugieren redundancia. Podemos considerar eliminar una de las características correlacionadas para reducir la multicolinealidad.
# Configuración de PyCaret
clf1 = setup(data=df,
target='articulacion',
session_id=123,
log_experiment=True,
experiment_name='clasificacion_articulacion',
preprocess=True, # Preprocesamiento automático
train_size=0.8) # Cómo dividir el conjunto de datos en PyCaret
# Comparar modelos automáticamente
best_model = compare_models()
best_model
| Description | Value | |
|---|---|---|
| 0 | Session id | 123 |
| 1 | Target | articulacion |
| 2 | Target type | Multiclass |
| 3 | Target mapping | otro: 0, pie: 1, rodilla: 2, tobillo: 3 |
| 4 | Original data shape | (717, 63) |
| 5 | Transformed data shape | (717, 63) |
| 6 | Transformed train set shape | (573, 63) |
| 7 | Transformed test set shape | (144, 63) |
| 8 | Numeric features | 62 |
| 9 | Preprocess | True |
| 10 | Imputation type | simple |
| 11 | Numeric imputation | mean |
| 12 | Categorical imputation | mode |
| 13 | Fold Generator | StratifiedKFold |
| 14 | Fold Number | 10 |
| 15 | CPU Jobs | -1 |
| 16 | Use GPU | False |
| 17 | Log Experiment | MlflowLogger |
| 18 | Experiment Name | clasificacion_articulacion |
| 19 | USI | 40bb |
| Model | Accuracy | AUC | Recall | Prec. | F1 | Kappa | MCC | TT (Sec) | |
|---|---|---|---|---|---|---|---|---|---|
| gbc | Gradient Boosting Classifier | 0.4102 | 0.0000 | 0.4102 | 0.4110 | 0.4033 | 0.2009 | 0.2028 | 0.0820 |
| rf | Random Forest Classifier | 0.4083 | 0.6258 | 0.4083 | 0.4099 | 0.3934 | 0.1938 | 0.1981 | 0.0200 |
| et | Extra Trees Classifier | 0.3979 | 0.6301 | 0.3979 | 0.4042 | 0.3826 | 0.1788 | 0.1824 | 0.0170 |
| lightgbm | Light Gradient Boosting Machine | 0.3806 | 0.6070 | 0.3806 | 0.3851 | 0.3760 | 0.1592 | 0.1607 | 1.3610 |
| xgboost | Extreme Gradient Boosting | 0.3770 | 0.6174 | 0.3770 | 0.3807 | 0.3732 | 0.1564 | 0.1577 | 0.0280 |
| dt | Decision Tree Classifier | 0.3718 | 0.5775 | 0.3718 | 0.3807 | 0.3705 | 0.1540 | 0.1556 | 0.0880 |
| ridge | Ridge Classifier | 0.3682 | 0.0000 | 0.3682 | 0.3612 | 0.3531 | 0.1403 | 0.1437 | 0.0040 |
| lr | Logistic Regression | 0.3543 | 0.0000 | 0.3543 | 0.3483 | 0.3454 | 0.1241 | 0.1257 | 0.1500 |
| lda | Linear Discriminant Analysis | 0.3508 | 0.0000 | 0.3508 | 0.3518 | 0.3446 | 0.1214 | 0.1232 | 0.0040 |
| ada | Ada Boost Classifier | 0.3211 | 0.0000 | 0.3211 | 0.3130 | 0.3119 | 0.0843 | 0.0851 | 0.0100 |
| knn | K Neighbors Classifier | 0.3018 | 0.5526 | 0.3018 | 0.2882 | 0.2876 | 0.0510 | 0.0519 | 0.0930 |
| dummy | Dummy Classifier | 0.2827 | 0.5000 | 0.2827 | 0.0799 | 0.1246 | 0.0000 | 0.0000 | 0.0050 |
| qda | Quadratic Discriminant Analysis | 0.2775 | 0.0000 | 0.2775 | 0.2796 | 0.2038 | 0.0120 | 0.0179 | 0.0040 |
| svm | SVM - Linear Kernel | 0.2758 | 0.0000 | 0.2758 | 0.2105 | 0.1817 | 0.0186 | 0.0215 | 0.0070 |
| nb | Naive Bayes | 0.2601 | 0.5317 | 0.2601 | 0.2199 | 0.1880 | 0.0074 | 0.0070 | 0.0860 |
GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='log_loss', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=100, n_iter_no_change=None,
random_state=123, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='log_loss', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=100, n_iter_no_change=None,
random_state=123, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False)rf = create_model('rf', fold=10)
| Accuracy | AUC | Recall | Prec. | F1 | Kappa | MCC | |
|---|---|---|---|---|---|---|---|
| Fold | |||||||
| 0 | 0.3793 | 0.5943 | 0.3793 | 0.3814 | 0.3590 | 0.1529 | 0.1578 |
| 1 | 0.4655 | 0.6127 | 0.4655 | 0.4444 | 0.4386 | 0.2709 | 0.2761 |
| 2 | 0.3966 | 0.6076 | 0.3966 | 0.4240 | 0.3797 | 0.1751 | 0.1802 |
| 3 | 0.4386 | 0.6480 | 0.4386 | 0.4582 | 0.4167 | 0.2294 | 0.2370 |
| 4 | 0.4211 | 0.6725 | 0.4211 | 0.4087 | 0.4094 | 0.2133 | 0.2150 |
| 5 | 0.3860 | 0.6101 | 0.3860 | 0.3960 | 0.3739 | 0.1701 | 0.1746 |
| 6 | 0.4035 | 0.6463 | 0.4035 | 0.4109 | 0.3995 | 0.1908 | 0.1934 |
| 7 | 0.3684 | 0.6373 | 0.3684 | 0.3191 | 0.3393 | 0.1334 | 0.1363 |
| 8 | 0.4211 | 0.6073 | 0.4211 | 0.4145 | 0.4132 | 0.2169 | 0.2185 |
| 9 | 0.4035 | 0.6220 | 0.4035 | 0.4422 | 0.4043 | 0.1847 | 0.1919 |
| Mean | 0.4083 | 0.6258 | 0.4083 | 0.4099 | 0.3934 | 0.1938 | 0.1981 |
| Std | 0.0276 | 0.0231 | 0.0276 | 0.0375 | 0.0284 | 0.0380 | 0.0383 |
plot_model(rf)
plot_model(rf, plot= 'confusion_matrix')
plot_model(rf, plot= 'feature')
plot_model(rf, plot= 'pr')
plot_model(rf, plot='class_report')
et = create_model('et', fold=10)
| Accuracy | AUC | Recall | Prec. | F1 | Kappa | MCC | |
|---|---|---|---|---|---|---|---|
| Fold | |||||||
| 0 | 0.4138 | 0.5873 | 0.4138 | 0.4463 | 0.4099 | 0.1971 | 0.2002 |
| 1 | 0.3793 | 0.6156 | 0.3793 | 0.3983 | 0.3593 | 0.1519 | 0.1554 |
| 2 | 0.4310 | 0.6341 | 0.4310 | 0.3624 | 0.3924 | 0.2242 | 0.2300 |
| 3 | 0.3860 | 0.6569 | 0.3860 | 0.4959 | 0.3692 | 0.1575 | 0.1641 |
| 4 | 0.3860 | 0.6533 | 0.3860 | 0.3688 | 0.3710 | 0.1635 | 0.1653 |
| 5 | 0.4737 | 0.6741 | 0.4737 | 0.4890 | 0.4669 | 0.2899 | 0.2963 |
| 6 | 0.4035 | 0.6449 | 0.4035 | 0.3900 | 0.3866 | 0.1843 | 0.1878 |
| 7 | 0.4035 | 0.6361 | 0.4035 | 0.3958 | 0.3866 | 0.1826 | 0.1855 |
| 8 | 0.3333 | 0.6004 | 0.3333 | 0.3318 | 0.3320 | 0.1016 | 0.1017 |
| 9 | 0.3684 | 0.5983 | 0.3684 | 0.3636 | 0.3523 | 0.1349 | 0.1372 |
| Mean | 0.3979 | 0.6301 | 0.3979 | 0.4042 | 0.3826 | 0.1788 | 0.1824 |
| Std | 0.0358 | 0.0272 | 0.0358 | 0.0525 | 0.0351 | 0.0491 | 0.0505 |
plot_model(et)
plot_model(et, plot= 'confusion_matrix')
plot_model(et, plot= 'feature')
df.columns
Index(['edad', 'sexo', 'altura', 'peso', 'num calzado', 'articulacion',
'pace_walk', 'velocidad_walk', 'step rate_walk', 'stride length_walk',
'shock_walk', 'impact gs_walk', 'braking gs_walk',
'footstrike type_walk', 'pronation excursion_walk',
'contact ratio_walk', 'total force rate_walk', 'step length_walk',
'pronation excursion (mp->to)_walk', 'stance excursion (fs->mp)_walk',
'stance excursion (mp->to)_walk', 'm1 hipermovil', 'thomas psoas',
'thomas rf', 'thomas tfl', 'ober', 'arco aplanado', 'arco elevado',
'm1 dfx', 'm5 hipermovil', 'arco transverso disminuido', 'm1 pfx',
'arco transverso aumentado', 'hlf', 'hl', 'hr', 'hav', 'index minus',
'tfi', 'tfe', 'tti', 'tte', 'ober friccion', 'popliteo', 't_hintermann',
'jack normal', 'jack no reconstruye', 'pronacion no disponible',
'2heel raise', 'heel raise', 'fpi_total_i', 'fpi_total_d',
'tibia vara proximal', 'tibia vara distal', 'rotula divergente',
'rotula convergente', 'rotula ascendida', 'genu valgo', 'genu varo',
'genu recurvatum', 'genu flexum', 'lunge', 'imc'],
dtype='object')
columns = ['edad', 'sexo', 'altura', 'peso', 'num calzado', 'articulacion',
'pace_walk', 'velocidad_walk', 'step rate_walk', 'stride length_walk', 'impact gs_walk', 'braking gs_walk',
'pronation excursion_walk', 'contact ratio_walk', 'total force rate_walk', 'step length_walk',
'pronation excursion (mp->to)_walk', 'stance excursion (fs->mp)_walk',
'stance excursion (mp->to)_walk', 'm1 hipermovil', 'thomas psoas',
'thomas tfl', 'arco aplanado', 'arco elevado', 'arco transverso disminuido', 'index minus',
'tfi', 'tfe', 'tti', 'tte', 'popliteo', 't_hintermann',
'jack normal', 'jack no reconstruye', '2heel raise', 'fpi_total_d',
'tibia vara proximal', 'rotula convergente', 'rotula ascendida', 'genu valgo', 'genu varo', 'lunge', 'imc']
# Configuración de PyCaret
clf1 = setup(data=df[columns],
target='articulacion',
session_id=123,
log_experiment=True,
experiment_name='clasificacion_articulacion',
preprocess=True, # Preprocesamiento automático
train_size=0.8) # Cómo dividir el conjunto de datos en PyCaret
# Comparar modelos automáticamente
best_model = compare_models()
best_model
| Description | Value | |
|---|---|---|
| 0 | Session id | 123 |
| 1 | Target | articulacion |
| 2 | Target type | Multiclass |
| 3 | Target mapping | otro: 0, pie: 1, rodilla: 2, tobillo: 3 |
| 4 | Original data shape | (717, 43) |
| 5 | Transformed data shape | (717, 43) |
| 6 | Transformed train set shape | (573, 43) |
| 7 | Transformed test set shape | (144, 43) |
| 8 | Numeric features | 42 |
| 9 | Preprocess | True |
| 10 | Imputation type | simple |
| 11 | Numeric imputation | mean |
| 12 | Categorical imputation | mode |
| 13 | Fold Generator | StratifiedKFold |
| 14 | Fold Number | 10 |
| 15 | CPU Jobs | -1 |
| 16 | Use GPU | False |
| 17 | Log Experiment | MlflowLogger |
| 18 | Experiment Name | clasificacion_articulacion |
| 19 | USI | d2a4 |
| Model | Accuracy | AUC | Recall | Prec. | F1 | Kappa | MCC | TT (Sec) | |
|---|---|---|---|---|---|---|---|---|---|
| rf | Random Forest Classifier | 0.4068 | 0.6288 | 0.4068 | 0.3997 | 0.3934 | 0.1920 | 0.1948 | 0.0210 |
| gbc | Gradient Boosting Classifier | 0.3962 | 0.0000 | 0.3962 | 0.3978 | 0.3889 | 0.1817 | 0.1838 | 0.0790 |
| et | Extra Trees Classifier | 0.3927 | 0.6237 | 0.3927 | 0.3797 | 0.3779 | 0.1735 | 0.1767 | 0.0140 |
| xgboost | Extreme Gradient Boosting | 0.3806 | 0.6121 | 0.3806 | 0.3847 | 0.3749 | 0.1602 | 0.1618 | 0.0230 |
| lightgbm | Light Gradient Boosting Machine | 0.3666 | 0.6113 | 0.3666 | 0.3719 | 0.3602 | 0.1414 | 0.1436 | 1.4270 |
| lda | Linear Discriminant Analysis | 0.3647 | 0.0000 | 0.3647 | 0.3615 | 0.3534 | 0.1370 | 0.1394 | 0.0040 |
| ridge | Ridge Classifier | 0.3543 | 0.0000 | 0.3543 | 0.3613 | 0.3409 | 0.1194 | 0.1224 | 0.0040 |
| lr | Logistic Regression | 0.3490 | 0.0000 | 0.3490 | 0.3521 | 0.3387 | 0.1153 | 0.1170 | 0.0130 |
| ada | Ada Boost Classifier | 0.3385 | 0.0000 | 0.3385 | 0.3281 | 0.3289 | 0.1054 | 0.1066 | 0.0100 |
| dt | Decision Tree Classifier | 0.3351 | 0.5572 | 0.3351 | 0.3388 | 0.3335 | 0.1055 | 0.1061 | 0.0050 |
| knn | K Neighbors Classifier | 0.3247 | 0.5597 | 0.3247 | 0.3126 | 0.3126 | 0.0839 | 0.0849 | 0.0040 |
| nb | Naive Bayes | 0.2932 | 0.5469 | 0.2932 | 0.3054 | 0.2508 | 0.0452 | 0.0504 | 0.0040 |
| svm | SVM - Linear Kernel | 0.2915 | 0.0000 | 0.2915 | 0.2508 | 0.1885 | 0.0385 | 0.0580 | 0.0060 |
| dummy | Dummy Classifier | 0.2827 | 0.5000 | 0.2827 | 0.0799 | 0.1246 | 0.0000 | 0.0000 | 0.0040 |
| qda | Quadratic Discriminant Analysis | 0.2686 | 0.0000 | 0.2686 | 0.3136 | 0.1904 | 0.0137 | 0.0157 | 0.0040 |
RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
criterion='gini', max_depth=None, max_features='sqrt',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
monotonic_cst=None, n_estimators=100, n_jobs=-1,
oob_score=False, random_state=123, verbose=0,
warm_start=False)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
criterion='gini', max_depth=None, max_features='sqrt',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
monotonic_cst=None, n_estimators=100, n_jobs=-1,
oob_score=False, random_state=123, verbose=0,
warm_start=False)columns = ['edad', 'sexo', 'imc', 'articulacion', 'pace_walk', 'stride length_walk', 'impact gs_walk',
'total force rate_walk', 'pronation excursion (mp->to)_walk', 'stance excursion (fs->mp)_walk']
# Configuración de PyCaret
clf1 = setup(data=df[columns],
target='articulacion',
session_id=123,
log_experiment=True,
experiment_name='clasificacion_articulacion',
preprocess=True, # Preprocesamiento automático
train_size=0.8) # Cómo dividir el conjunto de datos en PyCaret
# Comparar modelos automáticamente
best_model = compare_models()
best_model
| Description | Value | |
|---|---|---|
| 0 | Session id | 123 |
| 1 | Target | articulacion |
| 2 | Target type | Multiclass |
| 3 | Target mapping | otro: 0, pie: 1, rodilla: 2, tobillo: 3 |
| 4 | Original data shape | (717, 10) |
| 5 | Transformed data shape | (717, 10) |
| 6 | Transformed train set shape | (573, 10) |
| 7 | Transformed test set shape | (144, 10) |
| 8 | Numeric features | 9 |
| 9 | Preprocess | True |
| 10 | Imputation type | simple |
| 11 | Numeric imputation | mean |
| 12 | Categorical imputation | mode |
| 13 | Fold Generator | StratifiedKFold |
| 14 | Fold Number | 10 |
| 15 | CPU Jobs | -1 |
| 16 | Use GPU | False |
| 17 | Log Experiment | MlflowLogger |
| 18 | Experiment Name | clasificacion_articulacion |
| 19 | USI | 660e |
| Model | Accuracy | AUC | Recall | Prec. | F1 | Kappa | MCC | TT (Sec) | |
|---|---|---|---|---|---|---|---|---|---|
| et | Extra Trees Classifier | 0.3981 | 0.6172 | 0.3981 | 0.3863 | 0.3850 | 0.1814 | 0.1838 | 0.0150 |
| gbc | Gradient Boosting Classifier | 0.3787 | 0.0000 | 0.3787 | 0.3762 | 0.3715 | 0.1584 | 0.1598 | 0.0370 |
| lightgbm | Light Gradient Boosting Machine | 0.3701 | 0.6016 | 0.3701 | 0.3723 | 0.3656 | 0.1475 | 0.1490 | 1.1310 |
| rf | Random Forest Classifier | 0.3682 | 0.6181 | 0.3682 | 0.3686 | 0.3596 | 0.1411 | 0.1433 | 0.0180 |
| xgboost | Extreme Gradient Boosting | 0.3543 | 0.5920 | 0.3543 | 0.3539 | 0.3486 | 0.1247 | 0.1261 | 0.0150 |
| lda | Linear Discriminant Analysis | 0.3403 | 0.0000 | 0.3403 | 0.3393 | 0.3225 | 0.0994 | 0.1027 | 0.0050 |
| knn | K Neighbors Classifier | 0.3387 | 0.5902 | 0.3387 | 0.3286 | 0.3268 | 0.1010 | 0.1026 | 0.0060 |
| ridge | Ridge Classifier | 0.3351 | 0.0000 | 0.3351 | 0.2961 | 0.2930 | 0.0833 | 0.0905 | 0.0040 |
| dt | Decision Tree Classifier | 0.3315 | 0.5490 | 0.3315 | 0.3351 | 0.3267 | 0.0965 | 0.0977 | 0.0040 |
| lr | Logistic Regression | 0.3300 | 0.0000 | 0.3300 | 0.3168 | 0.2971 | 0.0787 | 0.0838 | 0.0100 |
| nb | Naive Bayes | 0.3175 | 0.5657 | 0.3175 | 0.3058 | 0.2711 | 0.0632 | 0.0744 | 0.0040 |
| qda | Quadratic Discriminant Analysis | 0.3175 | 0.0000 | 0.3175 | 0.3133 | 0.2926 | 0.0686 | 0.0736 | 0.0030 |
| ada | Ada Boost Classifier | 0.3158 | 0.0000 | 0.3158 | 0.3120 | 0.3103 | 0.0769 | 0.0776 | 0.0060 |
| dummy | Dummy Classifier | 0.2827 | 0.5000 | 0.2827 | 0.0799 | 0.1246 | 0.0000 | 0.0000 | 0.0040 |
| svm | SVM - Linear Kernel | 0.2776 | 0.0000 | 0.2776 | 0.1638 | 0.1624 | 0.0146 | 0.0256 | 0.0050 |
ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,
criterion='gini', max_depth=None, max_features='sqrt',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
monotonic_cst=None, n_estimators=100, n_jobs=-1,
oob_score=False, random_state=123, verbose=0,
warm_start=False)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,
criterion='gini', max_depth=None, max_features='sqrt',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
monotonic_cst=None, n_estimators=100, n_jobs=-1,
oob_score=False, random_state=123, verbose=0,
warm_start=False)et = create_model('et', fold=10)
| Accuracy | AUC | Recall | Prec. | F1 | Kappa | MCC | |
|---|---|---|---|---|---|---|---|
| Fold | |||||||
| 0 | 0.3621 | 0.5679 | 0.3621 | 0.3736 | 0.3514 | 0.1269 | 0.1297 |
| 1 | 0.4310 | 0.6115 | 0.4310 | 0.4144 | 0.4061 | 0.2232 | 0.2278 |
| 2 | 0.3103 | 0.5984 | 0.3103 | 0.2760 | 0.2886 | 0.0615 | 0.0630 |
| 3 | 0.4561 | 0.6305 | 0.4561 | 0.4282 | 0.4280 | 0.2585 | 0.2648 |
| 4 | 0.4912 | 0.6820 | 0.4912 | 0.4907 | 0.4889 | 0.3138 | 0.3147 |
| 5 | 0.3860 | 0.5837 | 0.3860 | 0.4096 | 0.3887 | 0.1681 | 0.1704 |
| 6 | 0.4386 | 0.6269 | 0.4386 | 0.4412 | 0.4370 | 0.2400 | 0.2412 |
| 7 | 0.3684 | 0.6317 | 0.3684 | 0.3341 | 0.3504 | 0.1393 | 0.1403 |
| 8 | 0.3684 | 0.6237 | 0.3684 | 0.3695 | 0.3673 | 0.1485 | 0.1490 |
| 9 | 0.3684 | 0.6154 | 0.3684 | 0.3257 | 0.3432 | 0.1342 | 0.1371 |
| Mean | 0.3981 | 0.6172 | 0.3981 | 0.3863 | 0.3850 | 0.1814 | 0.1838 |
| Std | 0.0515 | 0.0294 | 0.0515 | 0.0600 | 0.0543 | 0.0716 | 0.0720 |
plot_model(et)
plot_model(et, plot= 'confusion_matrix')
params = {'max_depth': range(1, 11), 'min_samples_split': range(2, 10), 'min_samples_leaf': range(1, 5)}
best_model = train_evaluate_tree_model(X_train, y_train, X_test, y_test, params, cv_folds=10)
best_model
Train Accuracy: 0.4887
Test Accuracy: 0.4306
Best Parameters: {'max_depth': 4, 'min_samples_leaf': 2, 'min_samples_split': 2}
DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
max_depth=4, max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_samples_leaf=2,
min_samples_split=2, min_weight_fraction_leaf=0.0,
monotonic_cst=None, random_state=0, splitter='best')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
max_depth=4, max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_samples_leaf=2,
min_samples_split=2, min_weight_fraction_leaf=0.0,
monotonic_cst=None, random_state=0, splitter='best')evaluate_model(best_model, X_test, y_test)
Predicciones realizadas con éxito Conversión a numpy arrays exitosa Cálculo de métricas exitoso Resultados para DecisionTreeClassifier: Accuracy: 0.4306 Precision: 0.3768 Recall: 0.4005 F1 Score: 0.3779
Visualización de la matriz de confusión exitosa
y_pred = best_model.predict(X_test) # Etiquetas predichas por el modelo
plot_confusion_matrix_with_metrics(y_test, y_pred)
Classification Report:
precision recall f1-score support
otro 0.42 0.50 0.46 38
pie 0.39 0.33 0.36 40
rodilla 0.51 0.70 0.59 40
tobillo 0.18 0.08 0.11 26
accuracy 0.43 144
macro avg 0.38 0.40 0.38 144
weighted avg 0.40 0.43 0.40 144
from sklearn import tree
fig = plt.figure(figsize=(25,20))
_ = tree.plot_tree(best_model,
feature_names=X_train.columns,
filled=True)
plot_feature_importance(best_model, X_train.columns)
columns = ['edad', 'sexo', 'imc', 'pace_walk', 'stride length_walk', 'impact gs_walk',
'total force rate_walk', 'pronation excursion (mp->to)_walk', 'stance excursion (fs->mp)_walk',
'step rate_walk', 'thomas tfl', 'tfi', 'index minus', 'arco aplanado']
params = {'max_depth': range(1, 11), 'min_samples_split': range(2, 10), 'min_samples_leaf': range(1, 5)}
best_model_2 = train_evaluate_tree_model(X_train[columns], y_train, X_test[columns], y_test, params, cv_folds=10)
best_model_2
Train Accuracy: 0.6684
Test Accuracy: 0.3542
Best Parameters: {'max_depth': 7, 'min_samples_leaf': 1, 'min_samples_split': 5}
DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
max_depth=7, max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_samples_leaf=1,
min_samples_split=5, min_weight_fraction_leaf=0.0,
monotonic_cst=None, random_state=0, splitter='best')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
max_depth=7, max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_samples_leaf=1,
min_samples_split=5, min_weight_fraction_leaf=0.0,
monotonic_cst=None, random_state=0, splitter='best')y_pred = best_model_2.predict(X_test[columns]) # Etiquetas predichas por el modelo
plot_confusion_matrix_with_metrics(y_test, y_pred)
Classification Report:
precision recall f1-score support
otro 0.33 0.18 0.24 38
pie 0.41 0.42 0.42 40
rodilla 0.41 0.60 0.48 40
tobillo 0.13 0.12 0.12 26
accuracy 0.35 144
macro avg 0.32 0.33 0.32 144
weighted avg 0.34 0.35 0.34 144
evaluate_model(best_model_2, X_test[columns], y_test)
Predicciones realizadas con éxito Conversión a numpy arrays exitosa Cálculo de métricas exitoso Resultados para DecisionTreeClassifier: Accuracy: 0.3542 Precision: 0.3213 Recall: 0.3311 F1 Score: 0.3161
Visualización de la matriz de confusión exitosa
columns = ['edad', 'sexo', 'altura', 'peso', 'num calzado', 'imc',
'pace_walk', 'velocidad_walk', 'step rate_walk', 'stride length_walk', 'impact gs_walk', 'braking gs_walk',
'pronation excursion_walk', 'contact ratio_walk', 'total force rate_walk', 'step length_walk',
'pronation excursion (mp->to)_walk', 'stance excursion (fs->mp)_walk',
'stance excursion (mp->to)_walk', 'm1 hipermovil', 'thomas psoas',
'thomas tfl', 'arco aplanado', 'arco elevado', 'arco transverso disminuido',
'tfi', 'tfe', 'tti', 'tte', 'popliteo', 't_hintermann', 'index minus',
'jack normal', 'jack no reconstruye', '2heel raise', 'fpi_total_d', 'tibia vara proximal',
'rotula convergente', 'rotula ascendida', 'genu valgo', 'genu varo', 'lunge']
params = {'max_depth': range(1, 11), 'min_samples_split': range(2, 10), 'min_samples_leaf': range(1, 5)}
best_model_3 = train_evaluate_tree_model(X_train[columns], y_train, X_test[columns], y_test, params, cv_folds=10)
best_model_3
Train Accuracy: 0.4887
Test Accuracy: 0.4306
Best Parameters: {'max_depth': 4, 'min_samples_leaf': 2, 'min_samples_split': 2}
DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
max_depth=4, max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_samples_leaf=2,
min_samples_split=2, min_weight_fraction_leaf=0.0,
monotonic_cst=None, random_state=0, splitter='best')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
max_depth=4, max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_samples_leaf=2,
min_samples_split=2, min_weight_fraction_leaf=0.0,
monotonic_cst=None, random_state=0, splitter='best')evaluate_model(best_model_3, X_test[columns], y_test)
Predicciones realizadas con éxito Conversión a numpy arrays exitosa Cálculo de métricas exitoso Resultados para DecisionTreeClassifier: Accuracy: 0.4306 Precision: 0.3768 Recall: 0.4005 F1 Score: 0.3779
Visualización de la matriz de confusión exitosa
y_pred = best_model_3.predict(X_test[columns]) # Etiquetas predichas por el modelo
plot_confusion_matrix_with_metrics(y_test, y_pred)
Classification Report:
precision recall f1-score support
otro 0.42 0.50 0.46 38
pie 0.39 0.33 0.36 40
rodilla 0.51 0.70 0.59 40
tobillo 0.18 0.08 0.11 26
accuracy 0.43 144
macro avg 0.38 0.40 0.38 144
weighted avg 0.40 0.43 0.40 144
fig = plt.figure(figsize=(25,20))
_ = tree.plot_tree(best_model_3,
feature_names=columns,
filled=True)
plot_feature_importance(best_model_3, X_train[columns].columns)
columns = ['total force rate_walk', 'pace_walk', 'step rate_walk', 'thomas tfl', 'genu valgo',
'pronation excursion (mp->to)_walk', 'thomas psoas', 'impact gs_walk', 'tfi',
'stride length_walk', 'edad', 'index minus','arco aplanado']
params = {'max_depth': range(1, 11), 'min_samples_split': range(2, 10), 'min_samples_leaf': range(1, 5)}
best_model_4 = train_evaluate_tree_model(X_train[columns], y_train, X_test[columns], y_test, params, cv_folds=10)
best_model_4
Train Accuracy: 0.4572
Test Accuracy: 0.4375
Best Parameters: {'max_depth': 3, 'min_samples_leaf': 1, 'min_samples_split': 2}
DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
max_depth=3, max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
monotonic_cst=None, random_state=0, splitter='best')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
max_depth=3, max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
monotonic_cst=None, random_state=0, splitter='best')evaluate_model(best_model_4, X_test[columns], y_test)
Predicciones realizadas con éxito Conversión a numpy arrays exitosa Cálculo de métricas exitoso Resultados para DecisionTreeClassifier: Accuracy: 0.4375 Precision: 0.3388 Recall: 0.4000 F1 Score: 0.3663
Visualización de la matriz de confusión exitosa
y_pred = best_model_4.predict(X_test[columns]) # Etiquetas predichas por el modelo
plot_confusion_matrix_with_metrics(y_test, y_pred)
Classification Report:
precision recall f1-score support
otro 0.40 0.50 0.44 38
pie 0.43 0.45 0.44 40
rodilla 0.53 0.65 0.58 40
tobillo 0.00 0.00 0.00 26
accuracy 0.44 144
macro avg 0.34 0.40 0.37 144
weighted avg 0.37 0.44 0.40 144
fig = plt.figure(figsize=(25,20))
_ = tree.plot_tree(best_model_4,
feature_names=columns,
filled=True)
plot_feature_importance(best_model_4, X_train[columns].columns)
1. Modelo 1 y Modelo 3:
F1 Score: 0.3779 (macro avg), 0.40 (weighted avg)
Ambos modelos son idénticos en términos de parámetros y métricas de desempeño, mostrando una precisión y recall moderados en comparación con los otros modelos. Son consistentes en términos de desempeño tanto en entrenamiento como en prueba.
2. Modelo 2:
F1 Score: 0.3161 (macro avg), 0.34 (weighted avg)
Aunque este modelo tiene la mayor precisión en el conjunto de entrenamiento, su rendimiento cae significativamente en el conjunto de prueba, indicando un posible sobreajuste.
3. Modelo 4:
F1 Score: 0.3663 (macro avg), 0.40 (weighted avg)
Este modelo tiene la mayor precisión en el conjunto de prueba y muestra una mejora leve sobre los modelos 1 y 3, aunque hay un desequilibrio significativo en la precisión y recall entre las categorías, destacando un desempeño pobre en la categoría 'tobillo' donde no se identificó correctamente ningún caso.
El Modelo 4 parece ser el más prometedor en términos de precisión general de prueba y manejo del desequilibrio entre las categorías de la variable objetivo. Aunque la precisión macro promedio es más baja, muestra la precisión más alta y consistente en el conjunto de prueba, lo que sugiere que es más robusto y generaliza mejor sobre datos no vistos en comparación con los otros modelos.
Selección de Características: El Modelo 4 utiliza un conjunto de características que incluye algunas de las más influyentes según los análisis de importancia de características (como total force rate_walk, pace_walk, stride length_walk, y impact gs_walk). Esta selección parece proporcionar un buen equilibrio entre la cantidad de información y el rendimiento del modelo, evitando posiblemente el sobreajuste que podríamos ver en el Modelo 1 que usa todas las características.
Rendimiento General: A pesar de que el Modelo 4 no tiene la mayor precisión en los entrenamientos (como el Modelo 2), muestra la mejor precisión de prueba, lo que indica que generaliza mejor en datos no vistos.
Complejidad del Modelo: El Modelo 4, con una selección más enfocada de características, probablemente sea menos complejo y más interpretable que el Modelo 1, que usa todas las características.
Balanced Feature Set: La elección de las características en el Modelo 4 parece estar bien equilibrada para capturar tanto los aspectos biomecánicos (ej., stride length_walk, total force rate_walk) como los factores de riesgo o condicionantes personales (ej., edad, genu valgo).
param_grid = {
'max_depth': range(1, 15),
'min_samples_split': [2, 5],
'min_samples_leaf': [1, 2],
'max_features': ['sqrt', 'log2']
}
rf = train_random_forest(X_train, y_train, X_train.columns, param_grid, n_jobs=1, cv=3)
Fitting 3 folds for each of 112 candidates, totalling 336 fits
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
Best mean cross-validation score: 0.366
Best parameters: {'max_depth': 6, 'max_features': 'sqrt', 'min_samples_leaf': 2, 'min_samples_split': 2}
evaluate_model(rf, X_test, y_test)
Predicciones realizadas con éxito Conversión a numpy arrays exitosa Cálculo de métricas exitoso Resultados para RandomForestClassifier: Accuracy: 0.3819 Precision: 0.3526 Recall: 0.3557 F1 Score: 0.3457
Visualización de la matriz de confusión exitosa
y_pred = rf.predict(X_test) # Etiquetas predichas por el modeloModel
plot_confusion_matrix_with_metrics(y_test, y_pred)
Classification Report:
precision recall f1-score support
otro 0.48 0.42 0.45 38
pie 0.34 0.45 0.39 40
rodilla 0.40 0.47 0.44 40
tobillo 0.18 0.08 0.11 26
accuracy 0.38 144
macro avg 0.35 0.36 0.35 144
weighted avg 0.37 0.38 0.37 144
plot_multiclass_roc(rf, X_test, y_test)
plot_feature_importance(rf, X_train.columns, figsize=(10,12))
columns = ['total force rate_walk', 'pace_walk', 'step rate_walk', 'thomas tfl', 'genu valgo',
'pronation excursion (mp->to)_walk', 'thomas psoas', 'impact gs_walk', 'tfi', 'contact ratio_walk',
'stride length_walk', 'edad', 'index minus','arco aplanado', 'stance excursion (fs->mp)_walk']
param_grid = {
'max_depth': range(1, 15),
'min_samples_split': [2, 5],
'min_samples_leaf': [1, 2],
'max_features': ['sqrt', 'log2']
}
rf2 = train_random_forest(X_train, y_train, columns, param_grid, n_jobs=1, cv=3)
Fitting 3 folds for each of 112 candidates, totalling 336 fits
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
Best mean cross-validation score: 0.379
Best parameters: {'max_depth': 8, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5}
evaluate_model(rf2, X_test[columns], y_test)
Predicciones realizadas con éxito Conversión a numpy arrays exitosa Cálculo de métricas exitoso Resultados para RandomForestClassifier: Accuracy: 0.4028 Precision: 0.3857 Recall: 0.3812 F1 Score: 0.3757
Visualización de la matriz de confusión exitosa
y_pred = rf2.predict(X_test[columns]) # Etiquetas predichas por el modeloModel
plot_confusion_matrix_with_metrics(y_test, y_pred)
Classification Report:
precision recall f1-score support
otro 0.42 0.42 0.42 38
pie 0.36 0.40 0.38 40
rodilla 0.46 0.55 0.50 40
tobillo 0.31 0.15 0.21 26
accuracy 0.40 144
macro avg 0.39 0.38 0.38 144
weighted avg 0.39 0.40 0.39 144
# Evaluación del modelo
train_accuracy = rf2.score(X_train[columns], y_train)
test_accuracy = rf2.score(X_test[columns], y_test)
print(f"Train Accuracy: {train_accuracy:.4f}")
print(f"Test Accuracy: {test_accuracy:.4f}")
Train Accuracy: 0.8517 Test Accuracy: 0.4028
plot_multiclass_roc(rf2, X_test[columns], y_test)
plot_feature_importance(rf2, X_train[columns].columns)
columns = ['total force rate_walk', 'pace_walk', 'step rate_walk', 'thomas tfl', 'genu valgo',
'pronation excursion (mp->to)_walk', 'thomas psoas', 'impact gs_walk', 'tfi', 'contact ratio_walk',
'stride length_walk', 'edad', 'index minus','arco aplanado', 'stance excursion (fs->mp)_walk']
param_grid = {
'max_depth': range(1, 15),
'min_samples_split': [2, 5],
'min_samples_leaf': [1, 2],
'max_features': ['sqrt', 'log2']
}
rf2 = train_random_forest_2(X_train, y_train, columns, param_grid, cv=5, n_jobs=1)
Fitting 5 folds for each of 112 candidates, totalling 560 fits
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
Best mean cross-validation score: 0.390
Best parameters: {'max_depth': 5, 'max_features': 'sqrt', 'min_samples_leaf': 1, 'min_samples_split': 5}
evaluate_model(rf2, X_test[columns], y_test)
Predicciones realizadas con éxito Conversión a numpy arrays exitosa Cálculo de métricas exitoso Resultados para RandomForestClassifier: Accuracy: 0.4306 Precision: 0.4287 Recall: 0.4338 F1 Score: 0.4305
Visualización de la matriz de confusión exitosa
columns = ['total force rate_walk', 'pace_walk', 'step rate_walk', 'thomas tfl', 'genu valgo',
'pronation excursion (mp->to)_walk', 'thomas psoas', 'impact gs_walk', 'tfi', 'contact ratio_walk',
'stride length_walk', 'edad', 'index minus','arco aplanado', 'stance excursion (fs->mp)_walk']
params = {
'max_depth': range(1, 15),
'min_samples_split': [2, 5],
'min_samples_leaf': [1, 2],
'max_features': ['sqrt', 'log2']
}
rf3 = train_random_forest_randomized(X_train, y_train, columns, params, cv=5, n_jobs=1)
Fitting 5 folds for each of 100 candidates, totalling 500 fits
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=9, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=9, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=2, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=4, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=6, max_features=log2, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=10, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=14, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=6, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=10, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.0s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=14, max_features=sqrt, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=sqrt, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=8, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=12, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.1s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=5, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=4, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.1s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=7, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=1, min_samples_split=5; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=1, max_features=sqrt, min_samples_leaf=2, min_samples_split=2; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=3, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.0s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=11, max_features=log2, min_samples_leaf=2, min_samples_split=5; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
[CV] END max_depth=13, max_features=log2, min_samples_leaf=1, min_samples_split=2; total time= 0.1s
Best mean cross-validation score: 0.390
Best parameters: {'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'sqrt', 'max_depth': 5}
evaluate_model(rf3, X_test[columns], y_test)
Predicciones realizadas con éxito Conversión a numpy arrays exitosa Cálculo de métricas exitoso Resultados para RandomForestClassifier: Accuracy: 0.4306 Precision: 0.4287 Recall: 0.4338 F1 Score: 0.4305
Visualización de la matriz de confusión exitosa
y_pred = rf3.predict(X_test[columns]) # Etiquetas predichas por el modeloModel
plot_confusion_matrix_with_metrics(y_test, y_pred)
Classification Report:
precision recall f1-score support
otro 0.50 0.47 0.49 38
pie 0.35 0.33 0.34 40
rodilla 0.46 0.47 0.47 40
tobillo 0.40 0.46 0.43 26
accuracy 0.43 144
macro avg 0.43 0.43 0.43 144
weighted avg 0.43 0.43 0.43 144
# Evaluación del modelo
train_accuracy = rf3.score(X_train[columns], y_train)
test_accuracy = rf3.score(X_test[columns], y_test)
print(f"Train Accuracy: {train_accuracy:.4f}")
print(f"Test Accuracy: {test_accuracy:.4f}")
Train Accuracy: 0.6719 Test Accuracy: 0.4306
plot_multiclass_roc(rf3, X_test[columns], y_test)
columns = ['total force rate_walk', 'pace_walk', 'step rate_walk', 'thomas tfl', 'genu valgo',
'pronation excursion (mp->to)_walk', 'thomas psoas', 'impact gs_walk', 'tfi', 'contact ratio_walk',
'stride length_walk', 'edad', 'index minus','arco aplanado', 'stance excursion (fs->mp)_walk']
# Configurar el RandomForestClassifier como el estimador base
rf_classifier = RandomForestClassifier(n_estimators=100, random_state=42, class_weight='balanced')
# Configurar el BaggingClassifier utilizando el RandomForest como estimador
bagging_model = BaggingClassifier(
estimator=rf_classifier,
n_estimators=10,
random_state=42
)
# Configuración para GridSearchCV
# Crear un GridSearchCV para optimizar max_depth del RandomForest
param_grid_bagging = {'estimator__max_depth': range(1, 15)}
grid_bagging = GridSearchCV(bagging_model, param_grid=param_grid_bagging, scoring='balanced_accuracy', cv=10, verbose=2)
grid_bagging.fit(X_train[columns], y_train)
# Imprimir los resultados
print("Best mean cross-validation score: {:.3f}".format(grid_bagging.best_score_))
print("Best parameters: {}".format(grid_bagging.best_params_))
# Extracción de resultados para visualización
scores = grid_bagging.cv_results_['mean_test_score']
max_depths = range(1, 15)
# Gráfica de los resultados
plt.figure(figsize=(8, 4))
plt.plot(max_depths, scores, '-o')
plt.xlabel('Max Depth of Base Estimator')
plt.ylabel('Balanced Accuracy')
plt.title('Performance vs Max Depth in Bagging')
plt.grid(True)
plt.show()
Fitting 10 folds for each of 14 candidates, totalling 140 fits
[CV] END .............................estimator__max_depth=1; total time= 0.4s
[CV] END .............................estimator__max_depth=1; total time= 0.4s
[CV] END .............................estimator__max_depth=1; total time= 0.4s
[CV] END .............................estimator__max_depth=1; total time= 0.4s
[CV] END .............................estimator__max_depth=1; total time= 0.4s
[CV] END .............................estimator__max_depth=1; total time= 0.4s
[CV] END .............................estimator__max_depth=1; total time= 0.4s
[CV] END .............................estimator__max_depth=1; total time= 0.4s
[CV] END .............................estimator__max_depth=1; total time= 0.4s
[CV] END .............................estimator__max_depth=1; total time= 0.4s
[CV] END .............................estimator__max_depth=2; total time= 0.4s
[CV] END .............................estimator__max_depth=2; total time= 0.4s
[CV] END .............................estimator__max_depth=2; total time= 0.4s
[CV] END .............................estimator__max_depth=2; total time= 0.4s
[CV] END .............................estimator__max_depth=2; total time= 0.4s
[CV] END .............................estimator__max_depth=2; total time= 0.5s
[CV] END .............................estimator__max_depth=2; total time= 0.4s
[CV] END .............................estimator__max_depth=2; total time= 0.4s
[CV] END .............................estimator__max_depth=2; total time= 0.4s
[CV] END .............................estimator__max_depth=2; total time= 0.4s
[CV] END .............................estimator__max_depth=3; total time= 0.4s
[CV] END .............................estimator__max_depth=3; total time= 0.4s
[CV] END .............................estimator__max_depth=3; total time= 0.4s
[CV] END .............................estimator__max_depth=3; total time= 0.4s
[CV] END .............................estimator__max_depth=3; total time= 0.4s
[CV] END .............................estimator__max_depth=3; total time= 0.4s
[CV] END .............................estimator__max_depth=3; total time= 0.4s
[CV] END .............................estimator__max_depth=3; total time= 0.4s
[CV] END .............................estimator__max_depth=3; total time= 0.4s
[CV] END .............................estimator__max_depth=3; total time= 0.4s
[CV] END .............................estimator__max_depth=4; total time= 0.4s
[CV] END .............................estimator__max_depth=4; total time= 0.4s
[CV] END .............................estimator__max_depth=4; total time= 0.4s
[CV] END .............................estimator__max_depth=4; total time= 0.4s
[CV] END .............................estimator__max_depth=4; total time= 0.4s
[CV] END .............................estimator__max_depth=4; total time= 0.4s
[CV] END .............................estimator__max_depth=4; total time= 0.4s
[CV] END .............................estimator__max_depth=4; total time= 0.4s
[CV] END .............................estimator__max_depth=4; total time= 0.4s
[CV] END .............................estimator__max_depth=4; total time= 0.4s
[CV] END .............................estimator__max_depth=5; total time= 0.4s
[CV] END .............................estimator__max_depth=5; total time= 0.4s
[CV] END .............................estimator__max_depth=5; total time= 0.4s
[CV] END .............................estimator__max_depth=5; total time= 0.4s
[CV] END .............................estimator__max_depth=5; total time= 0.4s
[CV] END .............................estimator__max_depth=5; total time= 0.4s
[CV] END .............................estimator__max_depth=5; total time= 0.4s
[CV] END .............................estimator__max_depth=5; total time= 0.4s
[CV] END .............................estimator__max_depth=5; total time= 0.4s
[CV] END .............................estimator__max_depth=5; total time= 0.4s
[CV] END .............................estimator__max_depth=6; total time= 0.5s
[CV] END .............................estimator__max_depth=6; total time= 0.5s
[CV] END .............................estimator__max_depth=6; total time= 0.5s
[CV] END .............................estimator__max_depth=6; total time= 0.5s
[CV] END .............................estimator__max_depth=6; total time= 0.5s
[CV] END .............................estimator__max_depth=6; total time= 0.5s
[CV] END .............................estimator__max_depth=6; total time= 0.5s
[CV] END .............................estimator__max_depth=6; total time= 0.5s
[CV] END .............................estimator__max_depth=6; total time= 0.5s
[CV] END .............................estimator__max_depth=6; total time= 0.5s
[CV] END .............................estimator__max_depth=7; total time= 0.5s
[CV] END .............................estimator__max_depth=7; total time= 0.5s
[CV] END .............................estimator__max_depth=7; total time= 0.5s
[CV] END .............................estimator__max_depth=7; total time= 0.5s
[CV] END .............................estimator__max_depth=7; total time= 0.5s
[CV] END .............................estimator__max_depth=7; total time= 0.5s
[CV] END .............................estimator__max_depth=7; total time= 0.5s
[CV] END .............................estimator__max_depth=7; total time= 0.5s
[CV] END .............................estimator__max_depth=7; total time= 0.5s
[CV] END .............................estimator__max_depth=7; total time= 0.5s
[CV] END .............................estimator__max_depth=8; total time= 0.5s
[CV] END .............................estimator__max_depth=8; total time= 0.5s
[CV] END .............................estimator__max_depth=8; total time= 0.5s
[CV] END .............................estimator__max_depth=8; total time= 0.5s
[CV] END .............................estimator__max_depth=8; total time= 0.5s
[CV] END .............................estimator__max_depth=8; total time= 0.5s
[CV] END .............................estimator__max_depth=8; total time= 0.5s
[CV] END .............................estimator__max_depth=8; total time= 0.5s
[CV] END .............................estimator__max_depth=8; total time= 0.5s
[CV] END .............................estimator__max_depth=8; total time= 0.5s
[CV] END .............................estimator__max_depth=9; total time= 0.5s
[CV] END .............................estimator__max_depth=9; total time= 0.5s
[CV] END .............................estimator__max_depth=9; total time= 0.5s
[CV] END .............................estimator__max_depth=9; total time= 0.5s
[CV] END .............................estimator__max_depth=9; total time= 0.5s
[CV] END .............................estimator__max_depth=9; total time= 0.5s
[CV] END .............................estimator__max_depth=9; total time= 0.5s
[CV] END .............................estimator__max_depth=9; total time= 0.5s
[CV] END .............................estimator__max_depth=9; total time= 0.5s
[CV] END .............................estimator__max_depth=9; total time= 0.5s
[CV] END ............................estimator__max_depth=10; total time= 0.5s
[CV] END ............................estimator__max_depth=10; total time= 0.5s
[CV] END ............................estimator__max_depth=10; total time= 0.5s
[CV] END ............................estimator__max_depth=10; total time= 0.5s
[CV] END ............................estimator__max_depth=10; total time= 0.5s
[CV] END ............................estimator__max_depth=10; total time= 0.5s
[CV] END ............................estimator__max_depth=10; total time= 0.5s
[CV] END ............................estimator__max_depth=10; total time= 0.5s
[CV] END ............................estimator__max_depth=10; total time= 0.5s
[CV] END ............................estimator__max_depth=10; total time= 0.5s
[CV] END ............................estimator__max_depth=11; total time= 0.5s
[CV] END ............................estimator__max_depth=11; total time= 0.5s
[CV] END ............................estimator__max_depth=11; total time= 0.5s
[CV] END ............................estimator__max_depth=11; total time= 0.6s
[CV] END ............................estimator__max_depth=11; total time= 0.5s
[CV] END ............................estimator__max_depth=11; total time= 0.5s
[CV] END ............................estimator__max_depth=11; total time= 0.5s
[CV] END ............................estimator__max_depth=11; total time= 0.5s
[CV] END ............................estimator__max_depth=11; total time= 0.5s
[CV] END ............................estimator__max_depth=11; total time= 0.5s
[CV] END ............................estimator__max_depth=12; total time= 0.5s
[CV] END ............................estimator__max_depth=12; total time= 0.5s
[CV] END ............................estimator__max_depth=12; total time= 0.5s
[CV] END ............................estimator__max_depth=12; total time= 0.5s
[CV] END ............................estimator__max_depth=12; total time= 0.5s
[CV] END ............................estimator__max_depth=12; total time= 0.5s
[CV] END ............................estimator__max_depth=12; total time= 0.6s
[CV] END ............................estimator__max_depth=12; total time= 0.5s
[CV] END ............................estimator__max_depth=12; total time= 0.5s
[CV] END ............................estimator__max_depth=12; total time= 0.5s
[CV] END ............................estimator__max_depth=13; total time= 0.6s
[CV] END ............................estimator__max_depth=13; total time= 0.6s
[CV] END ............................estimator__max_depth=13; total time= 0.6s
[CV] END ............................estimator__max_depth=13; total time= 0.6s
[CV] END ............................estimator__max_depth=13; total time= 0.6s
[CV] END ............................estimator__max_depth=13; total time= 0.6s
[CV] END ............................estimator__max_depth=13; total time= 0.6s
[CV] END ............................estimator__max_depth=13; total time= 0.6s
[CV] END ............................estimator__max_depth=13; total time= 0.6s
[CV] END ............................estimator__max_depth=13; total time= 0.5s
[CV] END ............................estimator__max_depth=14; total time= 0.5s
[CV] END ............................estimator__max_depth=14; total time= 0.5s
[CV] END ............................estimator__max_depth=14; total time= 0.6s
[CV] END ............................estimator__max_depth=14; total time= 0.6s
[CV] END ............................estimator__max_depth=14; total time= 0.6s
[CV] END ............................estimator__max_depth=14; total time= 0.5s
[CV] END ............................estimator__max_depth=14; total time= 0.5s
[CV] END ............................estimator__max_depth=14; total time= 0.5s
[CV] END ............................estimator__max_depth=14; total time= 0.5s
[CV] END ............................estimator__max_depth=14; total time= 0.5s
Best mean cross-validation score: 0.377
Best parameters: {'estimator__max_depth': 5}
best_bagging_model = grid_bagging.best_estimator_
print("Test Accuracy: {:.3f}".format(best_bagging_model.score(X_test[columns], y_test)))
print("Train: ",best_bagging_model .score(X_train[columns],y_train))
print("Test: ",best_bagging_model .score(X_test[columns],y_test))
Test Accuracy: 0.451 Train: 0.743455497382199 Test: 0.4513888888888889
evaluate_model(best_bagging_model, X_test[columns], y_test)
Predicciones realizadas con éxito Conversión a numpy arrays exitosa Cálculo de métricas exitoso Resultados para BaggingClassifier: Accuracy: 0.4514 Precision: 0.4502 Recall: 0.4526 F1 Score: 0.4499
Visualización de la matriz de confusión exitosa
y_pred = best_bagging_model.predict(X_test[columns]) # Etiquetas predichas por el modeloModel
plot_confusion_matrix_with_metrics(y_test, y_pred)
Classification Report:
precision recall f1-score support
otro 0.53 0.47 0.50 38
pie 0.42 0.38 0.39 40
rodilla 0.45 0.50 0.48 40
tobillo 0.40 0.46 0.43 26
accuracy 0.45 144
macro avg 0.45 0.45 0.45 144
weighted avg 0.45 0.45 0.45 144
plot_multiclass_roc(best_bagging_model, X_test[columns], y_test)
plot_feature_importances_bagging(best_bagging_model, columns)
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV
# Configuración de la búsqueda en malla para GradientBoostingClassifier
param_grid_gbc = {
#'n_estimators': [100, 200, 300],
'n_estimators': [100, 200],
'max_depth': range(3, 10),
'learning_rate': [0.01, 0.1, 0.2]
}
# Crear el modelo de GradientBoostingClassifier
gbc = GradientBoostingClassifier(random_state=42)
grid_gbc = GridSearchCV(estimator=gbc, param_grid=param_grid_gbc, cv=10, scoring='balanced_accuracy', verbose=2)
grid_gbc.fit(X_train[columns], y_train)
# Imprimir los mejores resultados de la validación cruzada
print("Best mean cross-validation score: {:.3f}".format(grid_gbc.best_score_))
print("Best parameters: {}".format(grid_gbc.best_params_))
Fitting 10 folds for each of 42 candidates, totalling 420 fits
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time= 1.0s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time= 1.0s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time= 1.0s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time= 1.0s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time= 1.0s
[CV] END ..learning_rate=0.01, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time= 1.5s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time= 1.5s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ..learning_rate=0.01, max_depth=6, n_estimators=200; total time= 1.5s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time= 0.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time= 1.1s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=100; total time= 1.2s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time= 2.3s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time= 2.3s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time= 2.4s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time= 2.3s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time= 2.3s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time= 2.3s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time= 2.4s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time= 2.4s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time= 2.4s
[CV] END ..learning_rate=0.01, max_depth=8, n_estimators=200; total time= 2.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time= 2.8s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time= 2.8s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time= 2.8s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time= 2.8s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time= 2.8s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time= 2.8s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time= 2.8s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time= 2.9s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time= 2.9s
[CV] END ..learning_rate=0.01, max_depth=9, n_estimators=200; total time= 2.8s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.1, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time= 1.0s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time= 1.0s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ...learning_rate=0.1, max_depth=4, n_estimators=200; total time= 1.0s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=100; total time= 0.7s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time= 1.4s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time= 1.3s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time= 1.3s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time= 1.3s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time= 1.3s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time= 1.3s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time= 1.3s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ...learning_rate=0.1, max_depth=5, n_estimators=200; total time= 1.3s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time= 1.5s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time= 1.5s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time= 1.7s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ...learning_rate=0.1, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time= 0.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time= 0.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time= 0.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time= 0.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time= 0.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time= 2.0s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time= 2.0s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ...learning_rate=0.1, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time= 1.1s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time= 1.1s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time= 1.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time= 1.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time= 1.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time= 1.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time= 1.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time= 1.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time= 1.1s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=100; total time= 1.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time= 2.3s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time= 2.3s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time= 2.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time= 2.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time= 2.2s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time= 2.3s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time= 2.3s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time= 2.3s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time= 2.6s
[CV] END ...learning_rate=0.1, max_depth=8, n_estimators=200; total time= 2.3s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time= 1.3s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time= 1.3s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time= 1.3s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time= 1.3s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time= 1.3s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time= 2.6s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time= 2.7s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time= 2.7s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time= 2.7s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time= 2.7s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time= 2.7s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time= 2.6s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time= 2.7s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time= 2.6s
[CV] END ...learning_rate=0.1, max_depth=9, n_estimators=200; total time= 2.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=100; total time= 0.4s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time= 0.8s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.2, max_depth=3, n_estimators=200; total time= 0.7s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=100; total time= 0.5s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time= 1.0s
[CV] END ...learning_rate=0.2, max_depth=4, n_estimators=200; total time= 0.9s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=100; total time= 0.6s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ...learning_rate=0.2, max_depth=5, n_estimators=200; total time= 1.2s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=100; total time= 0.8s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time= 1.5s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time= 1.6s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time= 1.5s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time= 1.5s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time= 1.5s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time= 1.5s
[CV] END ...learning_rate=0.2, max_depth=6, n_estimators=200; total time= 1.5s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time= 0.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time= 0.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time= 0.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time= 0.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=100; total time= 1.0s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time= 1.8s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time= 2.0s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ...learning_rate=0.2, max_depth=7, n_estimators=200; total time= 1.9s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time= 1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time= 1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time= 1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time= 1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time= 1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time= 1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time= 1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time= 1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time= 1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=100; total time= 1.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time= 2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time= 2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time= 2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time= 2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time= 2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time= 2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time= 2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time= 2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time= 2.1s
[CV] END ...learning_rate=0.2, max_depth=8, n_estimators=200; total time= 2.1s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time= 1.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time= 1.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time= 1.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time= 1.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time= 1.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time= 1.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time= 1.4s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time= 1.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=100; total time= 1.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time= 2.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time= 2.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time= 2.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time= 2.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time= 2.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time= 2.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time= 2.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time= 2.3s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time= 2.2s
[CV] END ...learning_rate=0.2, max_depth=9, n_estimators=200; total time= 2.3s
Best mean cross-validation score: 0.375
Best parameters: {'learning_rate': 0.1, 'max_depth': 4, 'n_estimators': 200}
# Ajustar el modelo óptimo
best_gbc = GradientBoostingClassifier(random_state=42, max_depth=grid_gbc.best_params_['max_depth'],
learning_rate=grid_gbc.best_params_['learning_rate'],
n_estimators=grid_gbc.best_params_['n_estimators'])
gbc = best_gbc.fit(X_train[columns], y_train)
# Evaluación del modelo
print("Train accuracy: {:.3f}".format(best_gbc.score(X_train[columns], y_train)))
print("Test accuracy: {:.3f}".format(best_gbc.score(X_test[columns], y_test)))
Train accuracy: 0.918 Test accuracy: 0.319
evaluate_model(gbc, X_test[columns], y_test)
Predicciones realizadas con éxito Conversión a numpy arrays exitosa Cálculo de métricas exitoso Resultados para GradientBoostingClassifier: Accuracy: 0.3194 Precision: 0.3124 Recall: 0.3093 F1 Score: 0.3082
Visualización de la matriz de confusión exitosa
y_pred = gbc.predict(X_test[columns]) # Etiquetas predichas por el modeloModel
plot_confusion_matrix_with_metrics(y_test, y_pred)
Classification Report:
precision recall f1-score support
otro 0.35 0.39 0.37 38
pie 0.29 0.28 0.28 40
rodilla 0.33 0.38 0.35 40
tobillo 0.28 0.19 0.23 26
accuracy 0.32 144
macro avg 0.31 0.31 0.31 144
weighted avg 0.32 0.32 0.32 144
plot_multiclass_roc(gbc, X_test[columns], y_test)